{"id":"W2168220711","doi":"10.1093/bioinformatics/bts209","title":"MoRFpred, a computational tool for sequence-based prediction and characterization of short disorder-to-order transitioning binding regions in proteins","year":2012,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":369,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"U.S. National Library of Medicine; Natural Sciences and Engineering Research Council of Canada; Killam Trusts; Russian Academy of Sciences; National Institutes of Health; National Science Foundation","keywords":"Sequence (biology); Support vector machine; Computer science; Computational biology; Intrinsically disordered proteins; Artificial intelligence; Chemistry; Biology; Biochemistry","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001147755,0.00007834074,0.00008412961,0.00007684351,0.00005221009,0.00001174227,0.00003807508,0.00008414896,6.398659e-7],"category_scores_gemma":[0.00003348497,0.00007825989,0.0000240893,0.0001070968,0.00002737103,0.00002397309,0.00001401307,0.00003214348,2.339058e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001292091,"about_ca_system_score_gemma":0.00004890958,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001719359,"about_ca_topic_score_gemma":0.000003764314,"domain_scores_codex":[0.9994707,0.000009784936,0.0002433262,0.0000689135,0.00007548504,0.0001317752],"domain_scores_gemma":[0.9997504,0.000007597586,0.00006036448,0.00007870535,0.00006633567,0.00003659248],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000258388,0.0001391046,0.05389807,0.0005417717,0.00005260684,1.476778e-7,0.001833571,0.0280237,0.8911421,0.001223988,0.00002221747,0.02286432],"study_design_scores_gemma":[0.002818728,0.0008595354,0.1231609,0.0003330855,0.0000667421,0.00002274567,0.0004572383,0.7798951,0.08960784,0.0002837604,0.001776503,0.0007178744],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5558724,0.0000075343,0.4434526,0.00003491233,0.0000235034,0.0004145982,0.0001747174,0.000004789862,0.0000149952],"genre_scores_gemma":[0.9065661,0.00000846668,0.09137856,0.00009008312,0.0000386523,0.00009005075,0.001814528,0.000007608282,0.000005974091],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8015343,"threshold_uncertainty_score":0.3191346,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01248384632375651,"score_gpt":0.2417982339169962,"score_spread":0.2293143875932397,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}